README.md

roahd

Package roahd (Robust Analysis of High-dimensional Data) allows to use
a set of statistical tools for the exploration and robustification of
univariate and multivariate functional datasets through the use of depth-based
statistical methods.

In the implementation of functions special attention was put to their efficiency,
so that they can be profitably used also for the analysis of high-dimensional
datasets.

(For a full-featured description of the package, please turn to the Vignette)

fData and mfData objects

A simple S3 representation of functional data object, fData,
allows to encapsulate the important features of univariate functional datasets (like the
grid of the dependent variable, the pointwise observations etc.):

Robust methods for functional data analysis

A part of the package is specifically devoted to the computation of depths and
other statistical indexes for functional data:

Band Dephts and Modified Band Depths,

Modified band depths for multivariate functional data,

Epigraph and Hypograph indexes,

Spearman and Kendall's correlation indexes for functional data.

These also are the core of the visualization/robustification tools like
functional boxplot (fbplot) and outliergram (outliergram), allowing
the visualization and identification of amplitude/shape outliers.

Thanks to the functions for the simulation of synthetic functional datasets,
both fbplot and outliergram procedures can be auto-tuned to the dataset
at hand, in order to control the true positive outliers rate.